Development of Electroencephalography (EEG) analysis ... · Agrawal, A. et al. (2005) “Design of...
1
UNCLASSIFIED UNCLASSIFIED EXPERIMENTAL METHODS FOR MULTI-GENRE NETWORKS Agrawal, A. et al. (2005) “Design of a two degree-of-freedom ankle-foot orthosis for robotic rehabilitation.”, ICORR 2005. (pp. 41-44). IEEE. Bradford, J. C. et al. (2016). “Electrocortical activity distinguishes between uphill and level walking in humans.”, Journal of neurophysiology, 115(2), 958-966. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21. Ehrlich, S. K. et al. (2018). “Human-agent co-adaptation using error-related potentials.”, Journal of neural engineering, 15(6) Li, J., Thakor, N. et al. (2018). “Unilateral exoskeleton imposes significantly different hemispherical effect in parietooccipital region, but not in other regions.” Scientific reports, 8(1), 1-10. Nordin, A. D. et al. (2018). “Dual-electrode motion artifact cancellation for mobile electroencephalography.”, Journal of neural engineering, 15(5) Oliveira, A. S. et al. (2016). “Induction and separation of motion artifacts in EEG data using a mobile phantom head device.” Journal of neural engineering, 13(3), 036014. Peterson, S. M., & Ferris, D. P. (2018). “Differentiation in theta and beta electrocortical activity between visual and physical perturbations to walking and standing balance.” eneuro, 5(4). Raghavendra, B. S. et al.(2011). “Wavelet enhanced CCA for minimization of ocular and muscle artifacts in EEG.”, World Academy of Science, Engineering and Technology, 57(6), 1027- 32. Richer, N. et al. (2020). “Motion and muscle artifact removal validation using an electrical head phantom, robotic motion platform, and dual layer mobile EEG.” IEEE Transactions on Neural Systems and Rehabilitation Engineering. Safieddine, D., et al.(2012). “Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches.” EURASIP Journal on Advances in Signal Processing, 2012(1), 127. Sawicki, G. S. et al.(2020). “The exoskeleton expansion: improving walking and running economy.” Journal of Neuro Engineering and Rehabilitation, 17(1), 1-9. Wagner, J. et al.(2016). “Distinct β band oscillatory networks subserving motor and cognitive control during gait adaptation.” Journal of Neuroscience, 36(7), 2212-2226. Walsh, C. J. et al. (2006, May). “Development of a lightweight, underactuated exoskeleton for load-carrying augmentation.”, ICRA 2006. (pp. 3485-3491). IEEE. Young, A. J. et al. (2016). “State of the art and future directions for lower limb robotic exoskeletons.”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(2), 171- 182. Zhang, J. et al. (2017). “Human-in-the-loop optimization of exoskeleton assistance during walking.”, Science, 356(6344), 1280-1284. Development of Electroencephalography (EEG) analysis pipeline for human and physical augmentation system interactions Introduction Method Results Conclusion Lower limb intelligent agents have been developed to augment human capability including mobility and proposed to assist soldiers for the battlefield (Sawicki et al, 2020, Walsh et al, 2006, Mooney et al, 2014). To maximize the effects of the intelligent agent system, the human-system co-adaptation is essential (Ehrlich et al, 2018, Agrawal et al, 2005). Wearable technologies have been achieved to adapt to the human movement in real-time(Zhang et al, 2017). On the contrary, a small number of studies have addressed the human interaction with the intelligent physical augmentation devices and are mostly focused on biomechanical measures of adaptation(Young and Ferries, 2016). Reference There is evidence (Bradford et al, 2016, Wagner et al, 2016, Peterson and Ferries, 2018, Li et al, 2018) that suggests brain signals may track how well humans are adapting to intelligent physical augmentation devices and that these signals may be used to improve the human- machine interface. We plan to use EEG to measure cortical electrical brain activity while people walk on a treadmill as they adapt to an intelligent physical augmentation device. EEG is known to have a poor signal to noise ratio (SNR) (Nordin et al, 2018) in ideal circumstances and whole-body movement while recording EEG has been shown to induce severe artifacts due to the motion of sensors relative to the scalp, electromagnetic interference (EMI), triboelectric effects, muscle activity, EKG, etc. further reducing the SNR. It is critical to develop a sufficient signal processing pipeline to ensure that these artifacts are extracted while not distorting the brain signal. The objective of this work was to develop a custom signal processing pipeline to extract brain signals from noisy EEG collected during our novel study paradigm. Lockheed Human Universal Load Carrier (HULC) exoskeleton http://www.lockheedmartin.com/content/da m/lockheed/data/mfc/photo/hulc/mfc-hulc-photo-01-h.jpg Study Protocol EEG processing pipeline By combining previously tested signal processing steps to generate a custom EEG processing pipeline, we were able to extract brain components as identified by power spectrum, topography, and good dipole fit (RV<15%) The identification of brain components is an indication that our pipeline was able to successfully separate brain signals from artifacts. It is nearly impossible to know whether our processing pipeline has distorted the brain signal, however each step in our custom pipeline has previously been tested and validated with either synthetic data or using an EEG phantom device and shown to preserve simulated brain signals (Safieddine et al 2012, Richer et al 2020,Oliveira et al 2016, Nordin et al 2018). The development of the EEG preprocessing pipeline will help us to understand brain dynamics while the human adapts to the intelligent physical augmentation device. Clean EEG channel data after applying EEG processing pipeline Raw EEG channel data (only 1 Hz high pass filter for visualization) (a) Time based EEG channel data after the 1Hz high pass filter(HPF) (b) EEG power spectrum after the 1Hz HPF (c) Time based EEG channel data after EEG signal processing (d) EEG power spectrum after the signal processing Brain components - Brain components 1 : Broadmann area 41 : Primary auditory cortex - Brain components 2 : Broadmann area 31 : low-level sensory or motor processing - Brain components 3 : Broadmann area 44 : language processing : Analyzing data offline using custom MATLAB(MathWorks, Natick, MA) scripts and EEGLAB (A Delorme et al, 2004). Song, Seongmi, Courtney Haynes, Anna K Mcgough, Christian Poindexter and J. Cortney Bradford* - Walk on the treadmill (walking speed 1.2m/s) with exoskeleton (Exo). - Study condition : Exo Off, Exo On, and Exo Trial conditions. - Measurement : EEG (128 EEG, 40 Noise & 8 EMG channels), EMG, and motion capture system (Nordin et al., 2018) (a) (c) (b) (d)
Transcript of Development of Electroencephalography (EEG) analysis ... · Agrawal, A. et al. (2005) “Design of...
UNCLASSIFIED
UNCLASSIFIED
EXPERIMENTAL METHODS FOR MULTI-GENRE NETWORKS
Agrawal, A. et al. (2005) “Design of a two degree-of-freedom ankle-foot orthosis for robotic rehabilitation.”, ICORR 2005. (pp. 41-44). IEEE. Bradford, J. C. et al. (2016). “Electrocortical activity distinguishes between uphill and level walking in humans.”, Journal of neurophysiology, 115(2), 958-966. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience
methods, 134(1), 9-21. Ehrlich, S. K. et al. (2018). “Human-agent co-adaptation using error-related potentials.”, Journal of neural engineering, 15(6) Li, J., Thakor, N. et al. (2018). “Unilateral exoskeleton imposes significantly different hemispherical effect in parietooccipital region, but not in other regions.” Scientific reports, 8(1), 1-10. Nordin, A. D. et al. (2018). “Dual-electrode motion artifact cancellation for mobile electroencephalography.”, Journal of neural engineering, 15(5) Oliveira, A. S. et al. (2016). “Induction and separation of motion artifacts in EEG data using a mobile phantom head device.” Journal of neural engineering, 13(3), 036014. Peterson, S. M., & Ferris, D. P. (2018). “Differentiation in theta and beta electrocortical activity between visual and physical perturbations to walking and standing balance.” eneuro, 5(4). Raghavendra, B. S. et al.(2011). “Wavelet enhanced CCA for minimization of ocular and muscle artifacts in EEG.”, World Academy of Science, Engineering and Technology, 57(6), 1027-
32. Richer, N. et al. (2020). “Motion and muscle artifact removal validation using an electrical head phantom, robotic motion platform, and dual layer mobile EEG.” IEEE Transactions on
Neural Systems and Rehabilitation Engineering. Safieddine, D., et al.(2012). “Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches.”
EURASIP Journal on Advances in Signal Processing, 2012(1), 127. Sawicki, G. S. et al.(2020). “The exoskeleton expansion: improving walking and running economy.” Journal of Neuro Engineering and Rehabilitation, 17(1), 1-9. Wagner, J. et al.(2016). “Distinct β band oscillatory networks subserving motor and cognitive control during gait adaptation.” Journal of Neuroscience, 36(7), 2212-2226. Walsh, C. J. et al. (2006, May). “Development of a lightweight, underactuated exoskeleton for load-carrying augmentation.”, ICRA 2006. (pp. 3485-3491). IEEE. Young, A. J. et al. (2016). “State of the art and future directions for lower limb robotic exoskeletons.”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(2), 171-
182. Zhang, J. et al. (2017). “Human-in-the-loop optimization of exoskeleton assistance during walking.”, Science, 356(6344), 1280-1284.
Development of Electroencephalography (EEG) analysis pipeline for human and
physical augmentation system interactions
Introduction
Method
Results
Conclusion
Lower limb intelligent agents have been developed to augment human capability including mobility and proposed to assist soldiers for the battlefield (Sawicki et al, 2020, Walsh et al, 2006, Mooney et al, 2014).
To maximize the effects of the intelligent agent system, the human-system co-adaptation is essential (Ehrlich et al, 2018, Agrawal et al, 2005).
Wearable technologies have been achieved to adapt to the human movement in real-time(Zhang et al, 2017).
On the contrary, a small number of studies have addressed the human interaction with the intelligent physical augmentation devices and are mostly focused on biomechanical measures of adaptation(Young and Ferries, 2016).
Reference
There is evidence (Bradford et al, 2016, Wagner et al, 2016, Peterson and Ferries, 2018, Li et al, 2018) that suggests brain signals may track how well humans are adapting to intelligent physical augmentation devices and that these signals may be used to improve the human-machine interface.
We plan to use EEG to measure cortical electrical brain activity while people walk on a treadmill as they adapt to an intelligent physical augmentation device.
EEG is known to have a poor signal to noise ratio (SNR) (Nordin et al, 2018) in ideal circumstances and whole-body movement while recording EEG has been shown to induce severe artifacts due to the motion of sensors relative to the scalp, electromagnetic interference (EMI), triboelectric effects, muscle activity, EKG, etc. further reducing the SNR.
It is critical to develop a sufficient signal processing pipeline to ensure that these artifacts are extracted while not distorting the brain signal.
The objective of this work was to develop a custom signal processing pipeline to extract brain signals from noisy EEG collected during our novel study paradigm.
Lockheed Human Universal Load Carrier (HULC) exoskeleton http://www.lockheedmartin.com/content/dam/lockheed/data/mfc/photo/hulc/mfc-hulc-photo-01-h.jpg
Study Protocol
EEG processing pipeline
By combining previously tested signal processing steps to generate a custom EEG processing pipeline, we were able to extract brain components as identified by power spectrum, topography, and good dipole fit (RV<15%)
The identification of brain components is an indication that our pipeline was able to successfully separate brain signals from artifacts.
It is nearly impossible to know whether our processing pipeline has distorted the brain signal, however each step in our custom pipeline has previously been tested and validated with either synthetic data or using an EEG phantom device and shown to preserve simulated brain signals (Safieddine et al 2012, Richer et al 2020,Oliveira et al 2016, Nordin et al 2018).
The development of the EEG preprocessing pipeline will help us to understand brain dynamics while the human adapts to the intelligent physical augmentation device.
Clean EEG channel data after applying EEG processing pipeline
Raw EEG channel data (only 1 Hz high pass filter for visualization)
(a) Time based EEG channel data after the 1Hz high pass filter(HPF) (b) EEG power spectrum after the 1Hz HPF (c) Time based EEG channel data after EEG signal processing (d) EEG power spectrum after the signal processing
Brain components
- Brain components 1 : Broadmann area 41: Primary auditory cortex
- Brain components 2: Broadmann area 31: low-level sensory or motor processing
- Brain components 3 : Broadmann area 44: language processing
: Analyzing data offline using custom MATLAB(MathWorks, Natick, MA) scripts and EEGLAB (A Delorme et al, 2004).
Song, Seongmi, Courtney Haynes, Anna K Mcgough, Christian Poindexter and J. Cortney Bradford*
- Walk on the treadmill (walking speed 1.2m/s) with exoskeleton (Exo).
- Study condition : Exo Off, Exo On, and Exo Trial conditions.
- Measurement : EEG (128 EEG, 40 Noise & 8 EMG channels), EMG, and motion capture system
(Nordin et al., 2018)
(a)
(c)
(b)
(d)
